This paper is in the following e-collection/theme issue:

Wearables and MHealth Reviews (273) mHealth for Wellness, Behavior Change and Prevention (2872) Diagnostic Tools in Mental Health (242) Digital Biomarkers and Digital Phenotyping (191) Methods and New Tools in Mental Health Research (454) mHealth for Diagnosis (60) mHealth for Screening (213) Voice Assistants (45) Depression and Mood Disorders; Suicide Prevention (1363) Speech and Phonetics in Verbal Protocols (Think Aloud, Recall, and Explanation Tasks) (93)

Published on in Vol 9, No 9 (2021): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24352, first published .
Using Acoustic Speech Patterns From Smartphones to Investigate Mood Disorders: Scoping Review

Using Acoustic Speech Patterns From Smartphones to Investigate Mood Disorders: Scoping Review

Using Acoustic Speech Patterns From Smartphones to Investigate Mood Disorders: Scoping Review

Authors of this article:

Olivia Flanagan1 Author Orcid Image ;   Amy Chan2 Author Orcid Image ;   Partha Roop3 Author Orcid Image ;   Frederick Sundram1 Author Orcid Image
Article Authors Cited by (10) Tweetations (3) Metrics

Journals

  1. Kim A, Jang E, Lee S, Choi K, Park J, Shin H. Automatic Depression Detection Using Smartphone-Based Text-Dependent Speech Signals: Deep Convolutional Neural Network Approach. Journal of Medical Internet Research 2023;25:e34474 View
  2. Tonn P, Seule L, Degani Y, Herzinger S, Klein A, Schulze N. Digital Content-Free Speech Analysis Tool to Measure Affective Distress in Mental Health: Evaluation Study. JMIR Formative Research 2022;6(8):e37061 View
  3. Martínez-Nicolás I, Martínez-Sánchez F, Ivanova O, Meilán J. Reading and lexical–semantic retrieval tasks outperforms single task speech analysis in the screening of mild cognitive impairment and Alzheimer's disease. Scientific Reports 2023;13(1) View
  4. Gerczuk M, Triantafyllopoulos A, Amiriparian S, Kathan A, Bauer J, Berking M, Schuller B. Zero-shot personalization of speech foundation models for depressed mood monitoring. Patterns 2023;4(11):100873 View
  5. Calà F, Frassineti L, Sforza E, Onesimo R, D’Alatri L, Manfredi C, Lanata A, Zampino G. Artificial Intelligence Procedure for the Screening of Genetic Syndromes Based on Voice Characteristics. Bioengineering 2023;10(12):1375 View
  6. Olawade D, Wada O, Odetayo A, David-Olawade A, Asaolu F, Eberhardt J. Enhancing mental health with Artificial Intelligence: Current trends and future prospects. Journal of Medicine, Surgery, and Public Health 2024;3:100099 View
  7. Shin J, Bae S. Use of voice features from smartphones for monitoring depressive disorders: Scoping review. DIGITAL HEALTH 2024;10 View
  8. Berisha V, Liss J. Responsible development of clinical speech AI: Bridging the gap between clinical research and technology. npj Digital Medicine 2024;7(1) View
  9. Mlakar I, Arioz U, Smrke U, Plohl N, Šafran V, Rojc M. An End-to-End framework for extracting observable cues of depression from diary recordings. Expert Systems with Applications 2024;257:125025 View
  10. Brown R, Bondy E, Prim J, Dichter G, Schiller C. The behavioral and physiological correlates of affective mood switching in premenstrual dysphoric disorder. Frontiers in Psychiatry 2024;15 View